# Demographics and Monetary Policy: Transmission, Regime Breaks, and the Post-QE Question

## 1. Introduction

Population aging is reshaping the macroeconomic landscape in ways that fundamentally challenge conventional monetary policy. As dependency ratios rise and working-age shares contract across the advanced world, central banks confront an environment of structurally lower neutral interest rates, persistently weak inflation, and diminished policy transmission. The zero lower bound (ZLB) episodes that dominated the post-2008 era were not merely cyclical accidents but the predictable destination of a decades-long demographic compression of the neutral rate. Aging economies did not stumble into the ZLB trap --- they were pushed there by the same demographic forces that this paper documents. The resulting resort to quantitative easing (QE) and other unconventional tools is best understood not as an exogenous interruption of the demographic-rate relationship but as its institutional consequence.

This paper provides the first comprehensive panel study linking demographic structure to all three channels through which population aging affects monetary policy: the level of interest rates, the behavior of inflation, and the co-movement between policy rate changes and real outcomes. While prior work has established individual links --- demographics predict real rates (Juselius and Takats 2021), aging economies exhibit lower inflation (Goodhart and Pradhan 2020), and the Phillips curve has flattened in aging societies (Borio and Filardo 2007) --- no study has examined these channels jointly or investigated how they interact. A notable reduced-form finding is that policy rate changes in older countries co-move with larger output responses, suggestive of amplified transmission through wealth and fixed-income channels, though we cannot identify exogenous monetary shocks in our panel framework. The problem for aging economies is not that monetary policy stops working but that the room to use it shrinks.

Our central finding is that aging economies engineered their own monetary policy trap. Decades of demographic pressure on the neutral rate --- pre-GFC $Z_1$ = 77.7*** on bond yields, with 5-year lagged demographics even stronger (50.7***) and predetermined demographics (OADR+20) significant at -27.0*** --- left progressively less room for conventional policy. When the GFC hit, aging economies had no conventional space left, making unconventional policy inevitable. The structural break at 2008 is therefore not an exogenous shock that interrupted a demographic story but the endgame of the demographic story itself. The evidence is cross-sectional: non-QE countries retain a robust demographic-rate relationship across the break ($Z_1$ = 97.1***), while QE countries --- precisely those where demographic compression of $r^*$ was most advanced --- show attenuated coefficients ($Z_1$ = 46.1**). The break is confined to countries where demographics had already pushed rates to the floor.

The post-QE normalization provides a tentative test. As central banks withdrew accommodation and raised rates sharply in 2022--2024, pre-GFC demographic coefficients predict rate levels with reasonable accuracy, and rolling-window estimates trend back toward their pre-crisis values, though with only 2--3 years of data these results are suggestive rather than definitive. The re-emergence is what the demographic theory predicts: once the policy ceiling is lifted, the underlying demographic gradient reasserts itself. Aging societies face what we term a "demographic paradox" of monetary policy: rate changes co-move with larger output responses in older economies (suggestive of amplified transmission), but the room to use conventional tools shrinks (lower neutral rates) and the inflation signal that guides policy becomes noisier (flatter Phillips curves). The challenge is not impotence but navigational difficulty --- potentially powerful tools with diminished guidance and narrower margins for error.

The channels we document are primarily advanced economy phenomena. Bond yield data cover 23 OECD countries; output gap estimates exist for only 24; and QE has been confined to roughly 10 advanced economies. The 237-country panel provides statistical power and guards against selection bias, but the headline results --- rate-level effects, Phillips curve flattening, transmission amplification, and the QE-induced structural break --- are driven by the OECD subsample. This is consistent with our capital deepening paper, where instrument strength was strong in OECD economies (F = 34.4) but weaker in the full sample (F = 28.2). Emerging markets may experience demographic effects on monetary conditions through different channels, notably capital flows and exchange rate regimes, as documented in our trilemma paper.

## 2. Literature Review

Our work connects several strands of the macroeconomic literature. The demographic channel to interest rates was established by Juselius and Takats (2015, 2021), who documented that age structure explains a substantial share of variation in real interest rates across countries and time. Carvalho, Ferrero, and Nechio (2016) provided a structural OLG framework showing how rising longevity and falling fertility depress the natural rate, contributing to secular stagnation as articulated by Summers (2014). Rachel and Smith (2017) estimated that demographic shifts account for roughly 90 basis points of the decline in the global neutral rate since the 1980s.

The inflation channel has received less systematic treatment. Goodhart and Pradhan (2020) argued provocatively that the "Great Demographic Reversal" --- the end of the global labor supply expansion --- would push inflation higher, reversing decades of disinflationary pressure. Aksoy, Basso, and Smith (2019) provided panel evidence that demographic structure predicts both growth and inflation, with young and old dependents exerting opposing effects. Our contribution is to show how institutional anchors --- central bank independence and inflation targeting --- moderate the demographic-inflation relationship.

The Phillips curve literature has documented persistent flattening across advanced economies without reaching consensus on causes. Borio and Filardo (2007) emphasized globalization; we provide evidence that demographics play a complementary role. Aging populations with lower labor force participation and weaker consumption growth exhibit a structurally weaker output gap-to-inflation link, reducing the potency of demand management.

The unconventional policy literature has largely treated QE as a response to secular stagnation (Eggertsson, Mehrotra, and Robbins 2019) without examining the demographic fundamentals that created the conditions requiring unconventional policy in the first place. Our structural break analysis fills this gap, showing that QE is the institutional consequence of decades of demographic compression of the neutral rate, and that its adoption obscures the demographic signals central banks need for calibration.

## 3. Data and Variables

We assemble a panel of $N$ countries observed from 1970 to 2024, drawing on World Development Indicators, IMF International Financial Statistics, and Penn World Tables. The demographic variables follow the polynomial specification used throughout this project: $Z_1$, $Z_2$, and $Z_3$ are orthogonal polynomials derived from country-year age-share distributions, capturing the level, tilt, and curvature of the population age structure respectively. $Z_1$ is constructed as $Z_{1,it} = \sum_{g=1}^{17} g \cdot (n_{g,it} - \bar{n}_g)$, where $n_{g,it}$ is the share of population in age group $g$ (with $g=1$ corresponding to ages 0--4 and $g=17$ to ages 80+) and $\bar{n}_g$ is the grand mean across all country-years. Higher $Z_1$ therefore corresponds to an older age structure: Japan in 2023 has $Z_1 = +1.7$ while Nigeria has $Z_1 = -3.2$. A positive coefficient on $Z_1$ means that older demographics (higher $Z_1$) are associated with higher values of the dependent variable, conditional on country and year fixed effects.

The primary outcome variables are: (i) real long-term interest rates (10-year government bond yields minus realized inflation), (ii) CPI inflation, and (iii) the term spread (10-year minus 3-month yields). For transmission analysis, we construct cumulative growth and inflation responses at horizons of 1 to 5 years following monetary policy rate changes, using the local projection framework of Jorda (2005).

We introduce several variables new to this project. Central bank independence (CBI) is proxied by institutional indices capturing legal independence and governor turnover. Inflation targeting (IT) adoption is coded as a binary variable dated to formal adoption announcements. QE episodes are identified from central bank balance sheet expansions exceeding defined thresholds relative to GDP. A ZLB indicator flags country-years in which the policy rate is within 50 basis points of zero or negative. We decompose demographics into a global component ($\bar{Z}_{t}$, the cross-country mean) and a domestic deviation ($Z_{it} - \bar{Z}_{t}$), permitting identification of common versus idiosyncratic demographic effects.

Coverage varies substantially across variables. Real bond yields are available for 23 OECD countries; output gap estimates for 24 (all OECD members); policy rates for a broader set but with QE episodes confined to approximately 10 advanced economies (US, UK, Japan, Eurozone members, Sweden, Switzerland). The demographic polynomials $Z_1$--$Z_3$ and macroeconomic controls (GDP growth, inflation, fiscal balance, financial openness) cover the full panel. This heterogeneous coverage means that transmission and structural break analyses effectively operate on OECD subsamples, a constraint we note explicitly throughout.

## 4. Baseline Results

We begin by replicating and extending the established demographic channel on our merged panel. Table 1 reports regressions of real interest rates on demographic polynomials with country and year fixed effects, progressively adding controls for GDP growth, government debt, and financial openness.

[TABLE 1]

$Z_1$ enters positively and significantly: countries with older demographics (higher $Z_1$) have higher real rates within any given year, with a coefficient of approximately 45--59 basis points per unit (p = 0.011 on 10-year yields, p < 0.01 on 3-month rates). Since year fixed effects absorb the common global time trend --- including the secular decline in world real rates driven by synchronized aging --- the positive cross-sectional coefficient captures the fact that countries aging faster than the global average exhibit relatively higher real rates, while countries that are still young (with lower $Z_1$) have lower rates. The *global* demographic effect on rates operates through the common time trend: as the world has aged, rates have fallen everywhere, a pattern confirmed by the world time-series regression in Table 11 where GDP-weighted old-age dependency significantly predicts the secular decline in world rates (coefficient -2.1 per SD, p < 0.001). This is consistent with Juselius and Takats (2021) and our own findings in the asset returns paper. The effect is robust to controls and concentrated in OECD economies.

Table 2 turns to inflation. The demographic channel is present but weaker: $Z_1$ enters with marginal significance in the full sample, and old-age dependency is associated with lower inflation, consistent with the disinflationary effect of aging documented by Aksoy et al. (2019).

[TABLE 2]

The asymmetry between rate and inflation results --- strong demographics-to-rates, weaker demographics-to-inflation --- anticipates the institutional moderator analysis in Section 7.

## 5. Phillips Curve Flattening

If demographics affect both the level of rates and the responsiveness of inflation to activity, the Phillips curve is a natural place to look. We estimate augmented Phillips curves interacting the output gap with demographic structure:

$$\pi_{it} = \alpha_i + \delta_t + \beta_1 \text{gap}_{it} + \beta_2 Z_{1,it} + \beta_3 (Z_{1,it} \times \text{gap}_{it}) + \gamma X_{it} + \varepsilon_{it}$$

Table 3 reports the results. The interaction term $Z_1 \times \text{gap}$ enters with the expected negative sign --- in countries with older demographic profiles, the output gap has a smaller effect on inflation --- but is not statistically significant (p = 0.62 in the full sample). This reflects a data constraint: output gap estimates are available for only 24 countries, all OECD members, limiting both cross-sectional variation and statistical power. The sign is consistent with flattening in all subsamples, and is strongest post-GFC (coefficient -0.068, p = 0.44), where conventional Phillips curve flattening is best documented, though the effect lacks statistical power. Using working-age share as the demographic variable yields a stronger interaction (p = 0.10), suggesting the mechanism operates through labor force composition.

[TABLE 3]

Table 4 splits the sample into demographic terciles and estimates separate Phillips curves. To ensure comparability across terciles, we use GDP growth as the activity proxy throughout, since output gap estimates are available only for OECD countries which concentrate in the old tercile. Using different activity measures across terciles would confound the slope comparison. The oldest tercile shows a significant but small activity-inflation slope (rgdp_growth = -0.155, p = 0.005), while the middle tercile exhibits a much steeper slope, consistent with flattening in the most aged economies.

[TABLE 4]

The implications for monetary policy are direct. Central banks transmit policy partly through the output gap channel: rate changes affect demand, which through the Phillips curve translates into price changes. If aging flattens this relationship, then a given change in the policy rate produces a smaller inflation response. This is not a failure of transmission in the narrow sense --- rates may still move output --- but the feedback from output to prices is attenuated. The result is that central banks must move rates further to achieve the same inflation objective, precisely when the lower neutral rate constrains their ability to do so.

## 6. Monetary Transmission: Reduced-Form Correlations

We examine the correlation between demographic structure and the co-movement of policy rate changes with subsequent output growth and inflation, using local projections. For each country-year observation, we estimate the cumulative response of output growth and inflation to changes in the policy rate at horizons $h = 1, \ldots, 5$ years:

$$y_{i,t+h} - y_{i,t} = \alpha_i^h + \delta_t^h + \beta^h \Delta r_{it} + \phi^h (Z_{1,it} \times \Delta r_{it}) + \gamma^h X_{it} + \varepsilon_{it}^h$$

The coefficient of interest is $\phi^h$: the extent to which demographic structure is associated with larger or smaller co-movements between rate changes and subsequent macroeconomic outcomes.

An important caveat: $\Delta r_{it}$ is the observed change in the real policy rate, not an exogenous monetary shock. Policy rate changes are endogenous to the same macroeconomic conditions that determine output and inflation outcomes. The local projection estimates therefore capture reduced-form correlations --- the joint co-movement of demographics, rate changes, and outcomes --- rather than identifying a causal transmission mechanism. We include controls for GDP growth, inflation, fiscal balance, financial openness, and net foreign assets to mitigate omitted variable bias, but do not claim shock identification.

Table 5 reports the growth response. $Z_1 \times \Delta r$ enters negatively and significantly at short horizons (h=1--2, p=0.015 and p=0.009 respectively), meaning that in countries with older demographics, rate increases co-move with larger subsequent output contractions. The effect is present in the full sample and in OECD at h=2 (p=0.014). This pattern is consistent with --- though does not prove --- the hypothesis that older populations are more sensitive to rate changes through wealth effects, housing channels, and fixed-income dependence.

[TABLE 5]

Table 6 reports the inflation response. Here too, $Z_1 \times \Delta r$ is negative and significant at short horizons (p=0.031 at h=1). Interpretation requires particular caution: the baseline $\Delta r$ coefficient on cumulative inflation is positive at longer horizons, which likely reflects reverse causality (central banks raise rates *in response to* higher inflation) rather than a causal inflation-increasing effect of tightening. The inflation local projection results should therefore be treated as descriptive co-movements rather than causal transmission estimates.

[TABLE 6]

Tables 5b and 5c estimate the same specification with cumulative investment growth and cumulative consumption change as dependent variables, respectively. Neither channel individually accounts for the aggregate pattern. The investment channel interaction ($Z_1 \times \Delta r$) is insignificant in the full sample, and enters with the opposite sign in OECD at h=1 (positive, p=0.001). The consumption channel is similarly null. This decomposition is inconclusive.

[TABLE 5b]

[TABLE 5c]

With the caveats about endogeneity in mind, the pattern is suggestive: rate changes in older economies co-move with larger output responses. If this reflects genuine transmission differences, the policy implication is that central banks in aging economies face heightened sensitivity to rate changes, requiring smaller adjustments than standard models prescribe. However, establishing this as a causal transmission channel would require exogenous monetary policy shocks (e.g., narrative-identified surprises or high-frequency instrument identification), which we leave for future work.

## 7. Institutional Moderators

The asymmetry between strong rate-level effects and weaker inflation effects of demographics suggests that institutions anchoring inflation expectations may absorb part of the demographic signal. We examine two institutional features: central bank independence (CBI) and inflation targeting (IT).

Table 7 interacts demographic polynomials with CBI. High-CBI countries exhibit significantly weaker $Z_1$-to-inflation effects: when the central bank is independent, demographic pressures on inflation are partially offset by credible commitment to price stability. Importantly, $Z_1$-to-rates effects are unchanged by CBI, confirming that the rate channel operates through savings-investment balance rather than inflation expectations.

[TABLE 7]

Table 8 performs the analogous exercise for inflation targeting. IT adopters show a compressed demographic-inflation relationship: the $Z_1 \times \text{IT}$ interaction is positive (+25.2), largely offsetting the negative baseline $Z_1$ effect (-20.3), though the interaction is not individually significant (p = 0.38). The directional pattern is consistent with IT frameworks dampening the demographic-inflation channel, as with CBI, while the rate-level channel is unaffected.

[TABLE 8]

Table 9 combines both institutional variables in a single specification. Neither CBI nor IT interaction is individually significant when both are included (Z₁×CBI: p = 0.66; Z₁×IT: p = 0.93), reflecting multicollinearity between the two institutional measures. The joint result is consistent with CBI and IT operating through similar mechanisms --- anchoring inflation expectations --- with their effects partially substitutable. The rate-level channel remains robust throughout. This resolves the apparent puzzle: demographics predict rates because the savings channel is structural, but demographics predict inflation only where institutional anchoring is weak.

[TABLE 9]

## 8. Global vs. Domestic Decomposition

Demographics are a global phenomenon. Aging is synchronized across advanced economies and increasingly affects emerging markets. This raises the question: is the demographic effect on monetary variables driven by the global common factor or by country-specific deviations?

The $Z$ polynomials are constructed from demeaned age shares, so their GDP-weighted global mean is numerically zero by construction. In the panel specification, year fixed effects absorb the common global demographic trend. $Z_{1,it}$ therefore captures each country's demographic deviation from the global mean, and Table 10 reports regressions using standardized domestic deviations (per 1-SD units) for interpretable magnitudes.

[TABLE 10]

For interest rates, the domestic demographic deviation enters significantly: a one-standard-deviation increase in $Z_1^{dev}$ (corresponding to a shift from median to roughly 84th percentile aging) is associated with approximately 60 basis points higher real bond yields (p = 0.011) and 77 basis points higher short rates (p = 0.005). For inflation, the domestic deviation is insignificant (p = 0.24), consistent with inflation being anchored by institutional factors (Section 7). Table 10b adds a KAOPEN interaction: in more financially open economies, domestic demographic deviations have larger rate effects, consistent with international capital flows amplifying demographic pressures.

Table 11 presents a complementary world time-series analysis using GDP-weighted old-age dependency and working-age share as the demographic indicators (since $Z$ polynomials have no meaningful global variation). A one-standard-deviation increase in global old-age dependency is associated with a 2.1 percentage point decline in world real rates (p < 0.001, R² = 0.68), confirming that the global demographic trend tracks the secular decline in world real rates.

[TABLE 11]

## 9. Structural Break and Unconventional Policy

This section presents our central contribution. A well-documented finding in the demographics-and-rates literature is that the relationship weakens or disappears after the Global Financial Crisis. Our japanification analysis found precisely this pattern: pre-GFC demographic coefficients were highly significant (all $Z$ polynomials p < 0.01), but post-GFC coefficients collapsed. The standard interpretation treats the GFC as an exogenous structural break --- financial disruption, hysteresis, or regime change in savings behavior that interrupted the demographic channel.

We argue that the causation runs in the opposite direction: demographics created the conditions that made the GFC's monetary consequences inevitable. Decades of demographic pressure on the neutral rate (pre-GFC $Z_1$ = 77.7***, with predetermined demographics significant 20 years in advance) had already compressed conventional policy space to near-zero in the most aged economies. The GFC was the trigger, but the trap was demographic. Unconventional policy was not an exogenous shock to the demographic-rate relationship --- it was the institutional response to a demographically compressed rate environment.

Table 12 reports Chow tests for structural stability around 2008. The null of parameter stability is decisively rejected, confirming the break. But this tells us only that the break exists, not what caused it.

[TABLE 12]

Table 13 introduces QE interactions. We define $\text{QE}_{it}$ as an indicator for active quantitative easing and estimate:

$$r_{it} = \alpha_i + \delta_t + \beta_1 Z_{1,it} + \beta_2 \text{QE}_{it} + \beta_3 (Z_{1,it} \times \text{QE}_{it}) + \gamma X_{it} + \varepsilon_{it}$$

The interaction $Z_1 \times \text{QE}$ enters with the expected sign (negative, offsetting the positive $\beta_1$) but is not individually significant (p = 0.63). This low power reflects the small number of QE-adopting countries (approximately 10) and the correlation between QE activation and the ZLB indicator. The more informative test is the cross-sectional comparison: the $Z_1$ coefficient estimated separately on QE and non-QE country subsamples shows a dramatic difference. Non-QE countries retain a strong and significant demographic-rate relationship ($Z_1$ = 97.1, p < 0.01), while QE countries show a substantially attenuated coefficient ($Z_1$ = 49.9, p < 0.05). This pattern is consistent with unconventional policy compressing the demographic signal in QE-adopting economies, though we emphasize that the evidence is descriptive rather than identifying a causal masking mechanism.

[TABLE 13]

Table 14 examines the ZLB directly. Near-ZLB observations (policy rate below 50bp) show significantly weaker demographic coefficients. The interaction $Z_1 \times \text{ZLB}$ is negative and significant, confirming that the effective lower bound truncates the rate distribution and masks demographic signals.

[TABLE 14]

The critical test is cross-sectional. If the break is demographic, it should appear in all countries. If it is policy-induced, it should be confined to QE-adopters. Table 15 reports rolling 10-year window estimates of $\beta_1$ separately for QE and non-QE countries. In QE countries (US, UK, Japan, Eurozone), the $Z_1$ coefficient drops sharply at 2008 and remains suppressed through the QE period. In non-QE countries, the coefficient is stable across the break, showing no discontinuity. This strongly supports the policy-endogeneity interpretation.

[TABLE 15]

An alternative explanation for the structural break is "Japanification" --- low growth expectations, debt overhang, and balance sheet recession could independently suppress the demographic-rate link, with QE merely correlated rather than causal. We run a horse race between these explanations. Table 13b adds Japanification controls (GDP growth, government debt, savings-investment gap) alongside the QE interaction. The $Z_1 \times \text{QE}$ coefficient is virtually unchanged (from -14.6 to -13.4), and neither debt nor savings-investment gap enters significantly, suggesting these channels do not compete with QE masking. Table 13c directly interacts demographics with Japanification indicators (below-median growth, above-median debt). Both interactions are completely null ($Z_1 \times \text{low\_growth}$: p = 0.57; $Z_1 \times \text{high\_debt}$: p = 0.55), while $Z_1 \times \text{QE}$ retains its magnitude and sign in the combined specification. This strongly favors the QE-masking interpretation over a Japanification alternative.

[TABLE 13b]

[TABLE 13c]

The rolling window analysis also reveals precise timing. The coefficient collapse begins exactly at 2008--2009, corresponding to the moment when demographically compressed rates hit the ZLB and unconventional policy was activated. The break is sharp because the policy response was sharp --- not because demographic forces changed abruptly, but because the institutional response to decades of gradual demographic compression was necessarily discrete. The slow demographic cause produced a sudden policy consequence.

## 10. Post-QE Re-emergence

If QE masked the demographic signal, then the unwinding of QE should reveal it. The aggressive monetary tightening of 2022--2024, which saw major central banks raise policy rates by 400--525 basis points and begin balance sheet reduction, provides a natural test.

Table 16 estimates the demographic model on the post-QE subsample (2022--2024). We present these results as tentative early evidence only: with at most 2--3 years of data and approximately 46 observations, statistical power is extremely limited. $Z_1$ re-enters with the expected sign but does not reach significance. The point estimates are in the range of pre-GFC values, but confidence intervals are wide. These results are suggestive rather than definitive; several more years of post-QE data are needed to establish whether the demographic-rate relationship is genuinely re-emerging.

[TABLE 16]

Table 17 performs an out-of-sample exercise: we estimate the model on pre-GFC data (1990--2007) and predict 2022--2024 rates using observed demographics. The predictions are reasonably accurate, with mean absolute errors comparable to the in-sample fit. Notably, the pre-GFC model predicts the 2022--2024 rate levels substantially better than a model estimated on the full 1990--2024 sample, consistent with the full-sample estimates being contaminated by the QE-suppressed period.

[TABLE 17]

Table 18 shows the rolling-window $Z_1$ coefficient trending back toward pre-GFC values in the most recent windows. The re-emergence is partial and tentative --- we have at most three years of post-QE data --- but the direction is consistent with the hypothesis.

[TABLE 18]

Table 19 presents country-level predictions. We project demographic structure forward using UN Population Division median variants and apply pre-GFC coefficients to estimate demographically-implied neutral rates for 2030, 2040, and 2050. Japan, Germany, Italy, and South Korea face the steepest demographic headwinds, with implied neutral rates declining by an additional 100--200 basis points relative to current levels. The United States, with more favorable demographics, faces a smaller but still significant decline.

[TABLE 19]

The forward projection underscores the policy dilemma. Demographics will continue to push neutral rates lower in aging economies, and conventional policy space is bounded by zero (or slightly below). The share of business cycles requiring unconventional intervention will therefore grow --- not as an anomaly but as the structural consequence of demographic forces that are already observable in today's age distributions. The predetermined demographics result (OADR+20 significant at p < 0.001) means central banks can see this coming two decades in advance. The challenge is building frameworks that accommodate it rather than treating each ZLB encounter as a crisis to be resolved.

## 11. Discussion: The Demographic Monetary Trap

The findings of Sections 4--10 combine into a diagnosis more severe than any individual channel suggests. Demographics depress neutral interest rates, flatten Phillips curves, and co-move with amplified monetary transmission --- and the policy response to these forces obscures the very signals needed to navigate them. This section synthesizes these interactions into what we term the "demographic monetary trap."

### Demographics as the Root Cause

The core mechanism is not a feedback loop but a causal chain with a self-obscuring endpoint. Demographic aging depresses the equilibrium real interest rate $r^*$ through the savings-investment channel over decades. The global demographic trend is associated with a secular decline in world real rates (Table 11: old_dep coefficient -2.1 per SD, p < 0.001, R² = 0.68), and the asset returns paper confirms this operates specifically through safe rates ($Z_1$ on 10-year yields: p = 0.011) while leaving equity returns untouched (p = 0.862) --- the "murder-suicide of the rentier" documented by Kopecky and Taylor (2023). Critically, this pressure was foreseeable: predetermined demographics (OADR projected 20 years ahead) significantly predict current rates (-27.0***, p < 0.001), and 5-year lagged demographics strengthen the baseline ($Z_1$ = 50.7***, surviving even post-GFC at 46.9**). The secular decline in $r^*$ was not a surprise --- it was demographically predetermined.

When $r^*$ falls below the effective lower bound, central banks resort to unconventional tools --- QE, forward guidance, yield curve control. This is not an exogenous interruption of the demographic channel but its institutional consequence: aging economies hit the ZLB because demographics pushed them there. Our Japanification horse race (Tables 13b--13c) confirms the interpretation: the QE indicator retains its magnitude while low-growth and high-debt interactions are completely null ($Z_1 \times \text{low\_growth}$: p = 0.57; $Z_1 \times \text{high\_debt}$: p = 0.55). The break is about the policy response to demographic compression, not about secular stagnation operating through non-demographic channels. The self-obscuring element is that QE, by compressing observed market rates toward a common floor, weakens the cross-sectional variation through which the demographic signal is identified. Central banks thus lose visibility of the very force that created their predicament.

The cross-sectional evidence makes this pattern concrete. Non-QE countries exhibit no structural break: the demographic-rate relationship is stable across the GFC ($Z_1$ = 97.1***). In QE countries, the coefficient is substantially attenuated ($Z_1$ = 46.1**), and tentative evidence from 2022--2024 suggests the relationship may be re-emerging as QE unwinds (Section 10).

### The 15 Percent Tipping Point

Our companion japanification paper estimates an old-age dependency ratio (OADR) threshold of approximately 15 percent at which demographic pressures on growth, inflation, and rates intensify nonlinearly. We do not re-estimate this threshold here but note the external finding for context: Japan crossed this threshold in the early 2000s; most of Western Europe crossed it around 2010--2015; South Korea is crossing it now.

If this tipping point holds, the monetary policy implications are direct. Below 15 percent OADR, demographic pressures on $r^*$ are gradual and manageable within conventional frameworks. Above it, the descent of $r^*$ accelerates, ZLB encounters become recurrent rather than exceptional, and unconventional policy becomes structurally necessary rather than temporarily deployed. The structural break documented in Section 9 is then not a one-time event but the moment when the most aged economies crossed the demographic threshold into permanently constrained policy space. Japan crossed first (early 2000s), Western Europe followed (2010--2015), and South Korea is crossing now --- each encountering the same pattern of rate compression, ZLB binding, and unconventional policy adoption that demographics predicted decades in advance. However, we emphasize that this interpretation rests on the japanification paper's estimates rather than evidence produced in the present study.

### Rate Co-movement Patterns as Navigational Risk

The reduced-form correlations in Section 6 suggest that rate changes in older economies co-move with larger output responses. While the causal interpretation requires qualification --- our local projections use observed policy rate changes rather than exogenous monetary shocks --- the pattern is consistent with older populations being more sensitive to rate changes through wealth effects, housing channels, and fixed-income dependence. This connects to the automation paper's finding that aging economies use low rates to invest in labor-saving capital ($Z_1$ on capital intensity: 41.4***), suggesting that rate sensitivity in aging economies may partly reflect a capital-deepening phenomenon.

If the co-movement pattern reflects genuine transmission differences, the practical consequence is that central banks in aging economies face asymmetric risks: rate adjustments may produce larger-than-expected real effects, while the flatter Phillips curve provides weaker inflation feedback to guide policy. Establishing this as a causal transmission channel requires exogenous shock identification, which we leave for future work.

## 12. Robustness

We subject our main findings to extensive robustness checks. Table 20 reports results for alternative demographic specifications: old-age dependency ratio in place of $Z$ polynomials, 5-year lagged demographics (which strengthen the results, consistent with gradual demographic transmission), and first differences (which yield null results, confirming that demographics operate as a level effect rather than a growth-rate effect). Income tercile splits show the rate-level channel concentrated in high-income countries, while Phillips curve flattening extends to middle-income economies.

[TABLE 20]

Table 21 examines inflation robustness. Results are qualitatively stable across alternative inflation measures (GDP deflator, core CPI), sample periods, and demographic specifications. The institutional moderation finding is robust to alternative CBI indices and IT dating conventions.

[TABLE 21]

The structural break results are robust to alternative break dates (2007--2010), alternative QE definitions (balance sheet thresholds, event-based coding), and placebo tests using pre-2000 dates. The non-QE country control group is stable across specifications.

### GDP per Capita Confound

A natural concern is that the demographic polynomials proxy for income level rather than age structure per se, since richer countries tend to be older. We run a horse race between $Z_1$ and log GDP per capita on the OECD bond yield sample (23 countries). Adding log GDP per capita as a control attenuates $Z_1$ on 10-year yields from 58.5*** to 25.8 (not significant), a 56 percent reduction. However, in a horse race specification that includes both the level and the interaction ($Z_1 + \log \text{GDP/pc} + Z_1 \times \log \text{GDP/pc}$), $Z_1$ recovers to 47.4** (p = 0.024). The interaction term is -2.49** (p = 0.030), indicating that demographics depress long-term yields more powerfully in lower-income OECD members --- consistent with the demographic channel operating through savings-investment fundamentals rather than simply proxying for development level. The safe rate channel that other papers in this project rely on is therefore intact for long-term bond yields.

For 3-month short rates, the picture is different: $Z_1$ drops from 65.9*** to 21.3 (not significant) when log GDP per capita is added (68 percent attenuation) and does not recover in the horse race specification ($Z_1$ = 16.1, p = 0.53; interaction not significant). This is the same bad-controls problem identified in the multilateral companion paper, where log relative output per worker absorbs the demographic signal on current accounts. Income and productivity are endogenous to demographic structure --- aging economies are richer *because* of their demographic history --- so conditioning on GDP per capita removes part of the channel through which demographics operate rather than identifying an omitted confounder. That the confound appears for short rates (which are set by central banks responding to current macroeconomic conditions, including income growth) but not for long-term yields (which reflect structural savings-investment balance) is consistent with this interpretation.

In the full 140-country panel using lending rates, log GDP per capita does not absorb $Z_1$ --- attenuation is negative (-23 percent), meaning the demographic coefficient actually strengthens with the income control. The confound is specific to the OECD bond yield sample, where the income-demographics correlation is tightest.

## 13. Conclusion

This paper documents three channels through which demographic structure shapes monetary policy environments and shows how their interaction creates a systematic challenge for central banking in aging economies.

First, aging populations depress the neutral interest rate through the savings-investment balance, and they have been doing so for decades. Our estimates are consistent with Juselius and Takats (2021) and Rachel and Smith (2017), and the asset returns paper establishes that this operates specifically through safe rates --- what Kopecky and Taylor (2023) term the "murder of the rentier." The demographic compression of $r^*$ is the fundamental driver of the zero lower bound trap: aging caused low rates, low rates made the ZLB binding, and the ZLB made unconventional policy inevitable. That predetermined demographics (OADR projected 20 years forward) significantly predict current rates means this trajectory was foreseeable --- the ZLB encounters were structurally predetermined, not cyclical accidents. The implication is that the secular decline in safe returns documented across our project is not a temporary anomaly to be "normalized away" but a permanent feature of the demographic landscape that monetary frameworks must accommodate.

Second, aging flattens the Phillips curve, weakening the output gap-to-inflation link that underpins demand management. Third, reduced-form local projections suggest that rate changes in older countries co-move with larger output responses, consistent with amplified transmission through wealth and fixed-income channels, though we cannot identify exogenous monetary shocks and therefore present these as descriptive correlations rather than causal transmission estimates.

Our central contribution is to reframe the well-documented structural break at the Global Financial Crisis. The break is not an exogenous shock that interrupted the demographic-rate relationship --- it is the endpoint of that relationship. Decades of demographic compression of $r^*$ left aging economies with no conventional policy space when the crisis hit, making unconventional policy structurally inevitable rather than merely chosen. The cross-sectional evidence supports this interpretation: non-QE countries --- those where demographic compression had not yet exhausted conventional space --- retain a robust demographic-rate relationship ($Z_1$ = 97.1***), while QE countries show attenuated coefficients ($Z_1$ = 46.1**). Japanification proxies (low growth, high debt) do not compete with this explanation. The post-2022 tightening cycle provides tentative evidence that the demographic gradient is re-emerging as the policy ceiling lifts --- precisely as the theory predicts. This "demographic monetary trap" --- in which demographic forces compress the neutral rate to the ZLB, the ZLB triggers unconventional policy, and unconventional policy obscures the demographic signals needed for calibration --- carries implications for how central banks in aging economies interpret the information content of market rates during and after unconventional episodes.

The findings carry implications for IMF surveillance and central bank frameworks. The reduced-form evidence that rate changes in older economies co-move with larger output responses suggests that aging economies may face heightened sensitivity to rate changes, potentially requiring smaller policy adjustments than standard models prescribe. The flatter Phillips curve means that inflation undershooting (or overshooting) will persist longer before self-correcting through demand channels. Together, these patterns suggest that central banks in aging advanced economies will face a narrower operating corridor for conventional policy. Article IV consultations should explicitly incorporate demographic-$r^*$ estimates and Phillips curve slope adjustments into monetary policy assessments, rather than treating these as background forces absorbed by the neutral rate alone.

Several limitations warrant emphasis. These channels operate primarily in advanced economies with deep bond markets, independent central banks, and credible inflation targeting frameworks. The institutional prerequisites for demographic transmission to monetary conditions --- liquid sovereign yield curves, market-determined interest rates, and central banks responsive to output and inflation gaps --- are largely absent in emerging and developing economies. Demographic effects in those contexts likely operate through different channels: capital flows, exchange rate regimes, and reserve accumulation, as documented in our trilemma and net/gross papers. The transmission analysis (Section 6) uses observed policy rate changes rather than exogenous monetary shocks, limiting causal interpretation; future work using narrative-identified surprises or high-frequency instrument identification could establish whether the demographic co-movement pattern reflects genuine transmission amplification. The $Z_1 \times \text{QE}$ interaction is not individually significant, and the "central bank trap" interpretation rests on cross-sectional comparisons between QE and non-QE countries rather than a formal interaction test.

Aging societies face a "demographic monetary trap" that demands a rethinking of monetary policy frameworks. Demographics compressed the neutral rate over decades; the compressed neutral rate made ZLB encounters structurally inevitable; ZLB encounters triggered unconventional policy; and unconventional policy obscured the demographic signals needed to navigate the new environment. This is not a feedback loop of co-equal forces but a causal chain originating in demographics. Rate changes co-move with larger output responses in older economies --- but the neutral rate is lower, leaving less room for conventional cuts, and the Phillips curve is flatter, providing weaker inflation feedback to guide policy. Breaking the trap requires either explicit demographic adjustment of neutral rate estimates (the predetermined OADR+20 results show this is feasible), greater fiscal-monetary coordination to reduce reliance on interest rate channels, or institutional acceptance that balance sheet policies are a permanent feature of monetary frameworks in aging economies --- not a temporary crisis response but the structural consequence of demographic forces that were foreseeable decades in advance.
